A Shift-Invariant Deep Learning Framework for Automated Analysis of XPS Spectra

TL;DR

This paper presents a deep learning framework using Spatial Transformer Networks to automatically analyze XPS spectra, effectively correcting spectral shifts and identifying chemical environments with high accuracy, advancing automated surface analysis techniques.

Contribution

Introduces a shift-invariant neural network model for XPS spectra classification, demonstrating effective correction of spectral shifts and high accuracy on synthetic data.

Findings

Effectively corrects spectral shifts up to 3.0 eV

Achieves approximately 82% accuracy in functional group identification

Uses a simple architecture outperforming previous complex models

Abstract

X-ray Photoelectron Spectroscopy (XPS) is a crucial technique for material surface analysis, yet interpreting its spectra is often challenging for both human analysts and automated methods due to the prevalence of variable spectral shifts and overlapping peaks. This project introduces a machine learning solution using a Spatial Transformer Network (STN), a type of neural network that implicitly learns to align spectra. An STN model was designed to classify the chemical environments present in an input spectrum, using functional groups as a proxy. The model was trained and tested on a large synthetic dataset of 100,000 spectra, created by linearly combining real experimental data from a library of 104 polymers. \cite{RN22} To simulate experimental variability, random uniform shifts and broadening were applied to the data. The STN was found to effectively correct for random electrostatic shifts (up to 3.0 eV) and achieved relatively high accuracy ($\sim$ 82\%) in identifying functional groups, despite utilizing a much simpler architecture than previous work. These findings demonstrate that neural networks can effectively learn the underlying relationships between spectral features and chemical composition when they are able to intrinsically account for variable shifts. This work advances the development of more reliable automated XPS analysis, offering potential as an assistive tool for researchers and as a core component in future autonomous systems like self-driving laboratories.